diff --git a/examples/training/train_tensorizer.py b/examples/training/train_tensorizer.py
index 65af7aaab..9720600f0 100644
--- a/examples/training/train_tensorizer.py
+++ b/examples/training/train_tensorizer.py
@@ -2,7 +2,7 @@
 import plac
 import spacy
 import thinc.extra.datasets
-from spacy.util import minibatch
+from spacy.util import minibatch, use_gpu
 import tqdm
 
 
@@ -12,7 +12,7 @@ def load_imdb():
     train_texts, _ = zip(*train)
     dev_texts, _ = zip(*dev)
     nlp.add_pipe(nlp.create_pipe('sentencizer'))
-    return list(get_sentences(nlp, train_texts)), list(get_sentences(nlp, dev_texts))
+    return list(train_texts), list(dev_texts)
 
 
 def get_sentences(nlp, texts):
@@ -21,12 +21,20 @@ def get_sentences(nlp, texts):
             yield sent.text
 
 
-def main():
+def prefer_gpu():
+    used = spacy.util.use_gpu(0)
+    if used is None:
+        return False
+    else:
+        return True
+
+def main(vectors_model):
+    use_gpu = prefer_gpu()
+    print("Using GPU?", use_gpu)
     print("Load data")
     train_texts, dev_texts = load_imdb()
-    train_texts = train_texts[:1000]
     print("Load vectors")
-    nlp = spacy.load('en_vectors_web_lg')
+    nlp = spacy.load(vectors_model)
     print("Start training")
     nlp.add_pipe(nlp.create_pipe('tagger'))
     tensorizer = nlp.create_pipe('tensorizer')
@@ -38,8 +46,7 @@ def main():
         for i, batch in enumerate(minibatch(tqdm.tqdm(train_texts))):
             docs = [nlp.make_doc(text) for text in batch]
             tensorizer.update(docs, None, losses=losses, sgd=optimizer, drop=0.5)
-            if i % 10 == 0:
-                print(losses)
+        print(losses)
 
 if __name__ == '__main__':
     plac.call(main)